9 papers
EmoWorld: A Decoupled Affective Field for Controllable Emotional Video Generation
Bingyuan Wang, Baistan Zhyldyzbekov, Kunyu Feng +1
Emotion shapes how viewers interpret a scene, yet existing video generators entangle global atmosphere, affect-bearing semantic cues, and temporal progression within a single text…
InstanceAnimator: Multi-Instance Sketch Video Colorization
Yinhan Zhang, Yue Ma, Bingyuan Wang +5
We propose InstanceAnimator, a novel Diffusion Transformer framework for multi-instance sketch video colorization. Existing methods suffer from three core limitations: inflexible u…
Controllable Video Generation: A Survey
Yue Ma, Kunyu Feng, Zhongyuan Hu +19
With the rapid development of AI-generated content (AIGC), video generation has emerged as one of its most dynamic and impactful subfields. In particular, the advancement of video…
EmoVid: A Multimodal Emotion Video Dataset for Emotion-Centric Video Understanding and Generation
Zongyang Qiu, Bingyuan Wang, Xingbei Chen +2
Emotion plays a pivotal role in video-based expression, but existing video generation systems predominantly focus on low-level visual metrics while neglecting affective dimensions.…
Follow-Your-Instruction: A Comprehensive MLLM Agent for World Data Synthesis
Kunyu Feng, Yue Ma, Xinhua Zhang +9
With the growing demands of AI-generated content (AIGC), the need for high-quality, diverse, and scalable data has become increasingly crucial. However, collecting large-scale real…
Follow-Your-Color: Multi-Instance Sketch Colorization
Yinhan Zhang, Yue Ma, Bingyuan Wang +2
We present Follow-Your-Color, a diffusion-based framework for multi-instance sketch colorization. The production of multi-instance 2D line art colorization adheres to an industry-s…